Developmental psychology and cultural evolution are concerned with the same research questions but rarely interact. Collaboration between these fields could lead to substantial progress. Developmental psychology and related fields such as educational science and linguistics explore how behavior and cognition develop through combinations of social and individual experiences and efforts. Human developmental processes display remarkable plasticity, allowing children to master complex tasks, many which are of recent origin and not part of our biological history, such as mental arithmetic or pottery. It is this potency of human developmental mechanisms that allow humans to have culture on a grand scale. Biological evolution would only establish such plasticity if the combinatorial problems associated with flexibility could be solved, biological goals be reasonably safeguarded, and cultural transmission faithful. We suggest that cultural information can guide development in similar way as genes, provided that cultural evolution can establish productive transmission/teaching trajectories that allow for incremental acquisition of complex tasks. We construct a principle model of development that fulfills the needs of both subjects that we refer to as Incremental Functional Development. This process is driven by an error-correcting mechanism that attempts to fulfill combinations of cultural and inborn goals, using cultural information about structure. It supports the acquisition of complex skills. Over generations, it maintains function rather than structure, and this may solve outstanding issues about cultural transmission. The presence of cultural goals gives the mechanisms an open architecture that become an engine for cultural evolution.

We introduce a mathematical model of cultural evolution to study cultural traits that shape how individuals exchange information. Current theory focuses on traits that influence the reception of information (receiver traits), such as evaluating whether information represents the majority or stems from a trusted source. Our model shifts the focus from the receiver to the sender of cultural information and emphasizes the role of sender traits, such as communicability or persuasiveness. Here, we show that sender traits are probably a stronger driving force in cultural evolution than receiver traits. While receiver traits evolve to curb cultural transmission, sender traits can amplify it and fuel the self-organization of systems of mutually supporting cultural traits, including traits that cannot be maintained on their own. Such systems can reach arbitrary complexity, potentially explaining uniquely human practical and mental skills, goals, knowledge and creativity, independent of innate factors. Our model incorporates social and individual learning throughout the lifespan, thus connecting cultural evolutionary theory with developmental psychology. This approach provides fresh insights into the trait-individual duality, that is, how cultural transmission of single traits is influenced by individuals, who are each represented as an acquired system of cultural traits.

Identifying cognitive capacities underlying the human evolutionary transition is challenging, and many hypotheses exist for what makes humans capable of, for example, producing and understanding language, preparing meals, and having culture on a grand scale. Instead of describing processes whereby information is processed, recent studies have suggested that there are key differences between humans and other animals in how information is recognized and remembered. Such constraints may act as a bottleneck for subsequent information processing and behavior, proving important for understanding differences between humans and other animals. We briefly discuss different sequential aspects of cognition and behavior and the importance of distinguishing between simultaneous and sequential input, and conclude that explicit tests on non-human great apes have been lacking. Here, we test the memory for stimulus sequences-hypothesis by carrying out three tests on bonobos and one test on humans. Our results show that bonobos’ general working memory decays rapidly and that they fail to learn the difference between the order of two stimuli even after more than 2,000 trials, corroborating earlier findings in other animals. However, as expected, humans solve the same sequence discrimination almost immediately. The explicit test on whether bonobos represent stimulus sequences as an unstructured collection of memory traces was not informative as no differences were found between responses to the different probe tests. However, overall, this first empirical study of sequence discrimination on non-human great apes supports the idea that non-human animals, including the closest relatives to humans, lack a memory for stimulus sequences. This may be an ability that sets humans apart from other animals and could be one reason behind the origin of human culture.

The Rescorla and Wagner (1972) model is the first mathematical theory to explain associative learning in the presence of multiple stimuli. Its main theoretical construct is that of associative strength, but this is connected to behavior only loosely. We propose a model in which behavior is described by a collection of Poisson processes, each with a rate proportional to an associative strength. The model predicts that the time between behaviors follows an exponential or hypoexponential distribution. This prediction is supported by two data sets on autoshaped and instrumental behavior in rats.

Relational responding refers to behavior that conforms to a rule for com- paring stimuli. Lazareva et al. (2014) trained pigeons to choose either the smaller or the larger of two circles, using 1–3 pairs of circles for training and 17–19 new pairs for testing. The pigeons showed relational responding on some test pairs and systematic failures on others. We present a simple artificial neural network model that reproduces the animals’ behavior well, similarly to Lazareva et al.’s (2014) statistical model based on stimulus features and stimulus relationships. We analyze how the network model gener- alizes from training to test stimuli, and show that it can reconcile contrasting ideas about relational responding from the seminal works by Köhler (1929, 1918/1938, 1924), positing that animals can learn relational rules such as “choose the larger stimulus,” and Spence (1937), positing that relational re- sponding can be explained based on stimulus generalization.

Abnormal behaviours are common in captive animals, and despite a lot of research, the development, maintenance and alleviation of these behaviours are not fully understood. Here, we suggest that conditioned reinforcement can induce sequential dependencies in behaviour that are difficult to infer from direct observation. We develop this hypothesis using recent models of associative learning that include conditioned reinforcement and inborn facets of behaviour, such as predisposed responses and motivational systems. We explore three scenarios in which abnormal behaviour emerges from a combination of associative learning and a mismatch between the captive environment and inborn predispositions. The first model considers how abnormal behaviours, such as locomotor stereotypies, may arise from certain spatial locations acquiring conditioned reinforcement value. The second model shows that conditioned reinforcement can give rise to abnormal behaviour in response to stimuli that regularly precede food or other reinforcers. The third model shows that abnormal behaviour can result from motivational systems being adapted to natural environments that have different temporal structures than the captive environment. We conclude that models including conditioned reinforcement offer an important theoretical insight regarding the complex relationships between captive environments, inborn predispositions, and learning. In the future, this general framework could allow us to further understand and possibly alleviate abnormal behaviours.

Human language is unique in its compositional, open-ended, and sequential form, and its evolution is often solely explained by advantages of communication. However, it has proven challenging to identify an evolutionary trajectory from a world without language to a world with language, especially while at the same time explaining why such an advantageous phenomenon has not evolved in other animals. Decoding sequential information is necessary for language, making domain-general sequence representation a tentative basic requirement for the evolution of language and other uniquely human phenomena. Here, using formal evolutionary analyses of the utility of sequence representation we show that sequence representation is exceedingly costly and that current memory systems found in animals may prevent abilities necessary for language to emerge. For sequence representation to evolve, flexibility allowing for ignoring irrelevant information is necessary. Furthermore, an abundance of useful sequential information and extensive learning opportunities are required, two conditions that were likely fulfilled early in human evolution. Our results provide a novel, logically plausible trajectory for the evolution of uniquely human cognition and language, and support the hypothesis that human culture is rooted in sequential representational and processing abilities.

Bridging associative and normative theories of animal learning, I show that an associative system can behave as if performing probabilistic inference by using the function f(V) = 1 − e^−cV to transform associative strengths (V) into response probabilities. For example, using this function, an associative system can respond normatively to a compound stimulus AB, given previous separate experiences with the components A and B. The CR probability formulae that result from the proposed function have a normative interpretation in terms of statistical decision theory. The formulae also suggest a normative interpretation of stimulus generalization as a heuristic to infer whether different stimuli are likely to convey redundant or independent information about reinforcement. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

In summation experiments, responding to a compound stimulus is assessed after conditioning a response to each of its components. This simple experiment poses significant challenges to models of associative learning because of substantial variability in results. Here, I introduce a new method to quantify generalization from components to compound in summation experiments, which I apply to over 250 measurements of summation in rabbits, pigeons, rats, and humans. The analysis confirms that more summation occurs with stimuli from different rather than from the same sensory modality, although this is not the sole determinant of summation. A theoretical analysis shows that this finding is best accounted for by a model that includes both element sharing (Rescorla & Wagner, 1972) and element replacement (Brandon et al., 2000) in stimulus representations. I point out remaining gaps in our empirical and theoretical understanding of summation. 

We present a new mathematical formulation of associative learning focused on non-human animals, which we call A-learning. Building on current animal learning theory and machine learning, A-learning is composed of two learning equations, one for stimulus-response values and one for stimulus values (conditioned reinforcement). A third equation implements decision-making by mapping stimulus-response values to response probabilities. We show that A-learning can reproduce the main features of: instrumental acquisition, including the effects of signaled and unsignaled non-contingent reinforcement; Pavlovian acquisition, including higher-order conditioning, omission training, autoshaping, and differences in form between conditioned and unconditioned responses; acquisition of avoidance responses; acquisition and extinction of instrumental chains and Pavlovian higher-order conditioning; Pavlovian-to-instrumental transfer; Pavlovian and instrumental outcome revaluation effects, including insight into why these effects vary greatly with training procedures and with the proximity of a response to the reinforcer. We discuss the differences between current theory and A-learning, such as its lack of stimulus-stimulus and response-stimulus associations, and compare A-learning with other temporal-difference models from machine learning, such as Q-learning, SARSA, and the actor-critic model. We conclude that A-learning may offer a more convenient view of associative learning than current mathematical models, and point out areas that need further development.